I am marching towards Synergetic & Holistic Intelligence.
My long term goal is to advance AI research and technologies in interrelated fields such as computer vision, machine learning, language understanding and robotics, to build intelligent systems, either virtual or embodied, to facilitate understanding multiple sensory inputs, to gain actionable insights from perception to cognition, to solve important real-world problems and to better serve our human race.
In the medium term, I am putting more emphasis on computer vision, machine learning and their applications, with a strong focus on accurate and efficient understanding of various types of objects and activities from sensory inputs such as images and videos. Over the past few years, I have explored a wide range of topics towards accurate and efficient visual understanding: from image-level classification, to instance-level object detection, to video-level detection and tracking, and more recently to spatio-temporal activity recognition etc. My team and I have been lucky to have won some international AI competitions and set new state-of-the-arts on major computer vision benchmarks. I am also fortunate to have been working on a broad spectrum of applied research projects with more than $10 million support, from research assistant, to team leader, and PI/Co-PI, with collaborators and support from industry, academic units and government agencies. This enables me to understand the true depth of challenges arose from real-world data and problems, or even in collaboration, management and technology transfer.
To emphasize, my current research focuses on accurate & efficient visual understanding for intelligent systems, in particular I have recently worked in:
- Computer Vision: classification, object detection, segmentation, activity recognition, etc.
- Machine Learning: weakly-supervised learning, transfer learning, multi-task learning, etc.
- AI Systems & Applications for Science, Education, Agriculture, Medcine, Finance, Transportation, etc.
My research activities include multiple aspects to solve such problems and to advance AI research: projects, papers, competitions, organizing workshops, training students etc.
Please find more publication and technical reports on Google Scholar.
Abbreviations: [C]: Conference; [J] Journal; [W] Workshop; [TR]: Technical Report; [B]: Book; [P]: Patent; [Comp]: Competition; [Proj]: Project; [SOTA]: State-of-the-art (at the time of publication)
- [TR] Revisiting Pre-training: An Efficient Training Method for Image Classification, Bowen Cheng, Yunchao Wei, Honghui Shi, Shiyu Chang, Jinjun Xiong, Thomas S. Huang, ArXiv Preprint, 2018
- [C] SpotTune: Transfer Learning through Adaptive Fine-tuning, Yunhui Guo, Honghui Shi, Abhishek Kumar, Kristen Grauman, Tajana Rosing, Rogerio Feris, CVPR, 2019 (SOTA on Visual Decathlon Challenge, acceptance rate 25.2 %)
- [C] Galaxy Classification Using Deep Convolutional Neural Networks, Honghui Shi, Thomas Huang, GTC, 2015
- [Comp] Galaxy Zoo - The Galaxy Challenge on Kaggle, Silver Medal (2014)
- [TR] Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection, Bowen Cheng, Yunchao Wei, Honghui Shi, Rogerio Feris, Jinjun Xiong, Thomas Huang, ArXiv Preprint, 2018
- [C] Learning Object Detection from Scratch via Gated Feature Reuse , Zhiqiang Shen, Honghui Shi, Jiahui Yu, Hai Phan, Rogerio Feris, Liangliang Cao, Ding Liu, Xinchao Wang, Thomas S. Huang, Marios Savvides, BMVC, 2019
- [C] Revisiting RCNN: On Awakening the Classification Power of Faster RCNN, Bowen Cheng, Yunchao Wei, Honghui Shi, Rogerio Feris, Jinjun Xiong, Thomas Huang, ECCV, 2018 (SOTA on PASCAL VOC, COCO, acceptance rate 31.8 %)
- [C] TS2C: Tight Box Mining with Surrounding Segmentation Context for Weakly Supervised Object Detection, Yunchao Wei, Zhiqiang Shen, Bowen Cheng, Honghui Shi, Jinjun Xiong, Jiashi Feng, Thomas Huang, ECCV, 2018 (acceptance rate 31.8 %)
- [TR] Learning Object Detectors from Scratch with Gated Recurrent Feature Pyramids, Zhiqiang Shen, Honghui Shi, Rogerio Feris, Liangliang Cao, Shuicheng Yan, Ding Liu, Xinchao Wang, Xiangyang Xue, Thomas S. Huang, ArXiv Preprint, 2017
- [C] Effective Object Detection from Traffic Camera Videos, Honghui Shi, Zhichao Liu, Yuchen Fan, Xinchao Wang, Thomas Huang, IEEE Smart World Congress, 2017 (Invited paper)
- [Comp] Nvidia AI City Challenge 1st Place (2017)
- [W] Improving Context Modeling for Video Object Detection and Tracking, Yunchao Wei, Mengdan Zhang, Jianan Li, Yunpeng Chen, Jiashi Feng, Honghui Shi, Jian Dong, Shuicheng Yan, Beyond ImageNet Large Scale Visual Recognition Challenge @ CVPR, 2017
- [Comp] ImageNet Video Object Detection and Tracking Challenge 2nd Place (2017)
- [TR] Seq-NMS for Video Object Detection, Wei Han, Pooya Khorrami, Tom Le Paine, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang, ArXiv Preprint, 2016
- [Comp] ImageNet Video Object Detection Challenge 3rd Place (2015)
- [C] SPGNet: Semantic Prediction Guidance for Scene Parsing, Bowen Cheng, Liang-Chieh Chen, Yunchao Wei, Yukun Zhu, Zilong Huang, Jinjun Xiong, Thomas Huang, Wen-mei Hwu, Honghui Shi, ICCV, 2019 (SOTA on Cityscapes, acceptance rate 25.0 %)
- [C] Geometry-Aware Distillation for Indoor Semantic Segmentation, Jianbo Jiao, Yunchao Wei, Zequn Jie, Honghui Shi, Rynson W.H. Lau, Thomas S. Huang, CVPR, 2019 (acceptance rate 25.2 %)
- [C] Weakly Supervised Scene Parsing with Point-based Distance Metric Learning, Rui Qian, Yunchao Wei, Honghui Shi, Jiachen Li, Jiaying Liu, Thomas Huang, AAAI, 2019 (acceptance rate 16.2 %)
- [C] Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation, Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, Thomas S. Huang, CVPR, 2018 (Spotlight Oral, acceptance rate 6.7 %)
- [Proj] Deep Intermodal Video Analytics (DIVA), sponsored by IARPA, 2017.10 - 2021.09
- [W] Object-Centric Spatio-Temporal Activity Detection and Recognition, Mandis Beigi, Lisa M Brown, Quanfu Fan, John Henning, Chung-Ching Lin, Honghui Shi, Chiao-fe Shu, Rogerio Feris, NIST TRECVID Workshop, 2018
- [Comp] NIST/IARPA TRECVID Activity Recognition Challenge 1st Place (2018)
AI Systems & Applications:
- [TR] SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems, Xiaofan Zhang, Haoming Lu, Cong Hao, Jiachen Li, Bowen Cheng, Yuhong Li, Kyle Rupnow, Jinjun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen, ArXiv Preprint, 2019
- [TR] SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection, Xiaofan Zhang, Cong Hao, Haoming Lu, Jiachen Li, Yuhong Li, Yuchen Fan, Kyle Rupnow, Jinjun Xiong, Thomas Huang, Honghui Shi, Wen-mei Hwu, Deming Chen, ArXiv Preprint, 2019
- [Comp] IEEE/ACM DAC System Design Contest 1st Place (2019)
- [Proj] AI for Education, sponsored by New Oriental Education Technology
- [Proj] Deep Pattern Analysis in Agricultural Images, sponsored by IntelinAir
- [Proj] Multiphoton Image Analysis for Cancer Diagnosis, sponsored by Mayo Clinic & UIUC
- [Proj] Intelligent Learning Advisor, sponsored by IBM Research
- [Proj] Multi-modal Medical Image Understanding, sponsored by Jump ARCHES
- [Proj] Deep Learning in Financial Modeling and Strategy, sponsored by Jump Trading
- [Proj] Galaxy Classification and Gravitational Lens Detection, collaborated with UIUC Astronomy